Table REF  compares the sum-rate performance of all techniques. The best sum-rate is obtained by O-FDP since the RF structure is not constrained, but this solution is not energy-efficient compared to HBF. The DNN architecture implementing the FC-HBF structure achieves \(95 \%\)  of FDP sum-rate, while improving the sum-rate by \(15\%\)  when compared to the MO-AltMin solution [1]}.
FSA-HBF-Net outperforms existing methods by \(19\%\) . Furthermore, the DSA-HBF-Net approximately reaches \(92\%\)  of FC-HBF sum-rate, while the number of PS has been divided by a factor \(N_{\sf {RF}}\) . For DSA-HBF-Net, the Gumbel-Softmax temperature is set to \(\tau = 1.5\) . This choice will be motivated later in this section. When compared to “Dynamic Subarray Partitioning” in [2]}, the proposed approach improves the sum-rate by \(7\%\) .
Note that all conventional methods were evaluated using perfect CSI, whereas the proposed DNN techniques consider noisy CSI in the evaluation phase. Therefore, it is expected that the performance gap would be larger in practice.
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